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Data Science in business- Tech meetups with McKinsey& Company

Join our Wroclaw community of data practitioners on Tuesdays from 18:30 – 20:30 in the McKinsey Knowledge Center for interactive technical presentations by data scientists, tech entrepreneurs and experts. All presentations will be facilitated by our internal or external speakers, and followed by a networking reception. Space is limited.

When:Select Tuesdays, 18:30 – 20:30

Where:McKinsey Polish Knowledge Center
Plac Grunwaldzki 23-27
Wroclaw 50–365

2 July, 18:30 – 20:30

Time series forecasting: best practices & recent advances

Although time series forecasting is the part of broad family of predictive analytics and supervised learnings methods, it's also quite different from most commonly known Machine Learning techniques. Historically, times series were forecasted mainly by simple univariate methods like Exponential Smoothing or ARIMA models. Nowadays, the abundance of data and methods encourage us to use more advanced multivariate methods known very well from machine learning domain. However, forecasting time series differs in a few significant aspects. Firstly, times series data differ from cross-section data, which is used usually in Machine Learning applications, due to non-stationary and non-IID (independent and identically distributed) properties of data generating process. Secondly, while Machine Learning problems are usually concerned with the classification problems, times series forecasting is focused more on regression problems, with the increasing importance of not only point estimates themselves, but also the distribution and confidence intervals of forecasted phenomena. Thirdly, seasonality is the another inherent aspect of times series, rarely encountered in cross-section and machine learning problems. Therefore, modern forecasting of time series requires data scientists to take advantage of two analytical worlds, i.e. (1) classical univariate time series toolbox accounting for peculiarities of times series data and (2) Machine-Learning-based models. Being fluent only in one toolbox will result in leaving money on the table. The aim of talk is to provide you with the overview of time series forecasting toolbox and snippets of R code enabling you to tackle some typical challenges faced while modelling times series data. Secondly, the talk will highlight some recent advances made in times series research.

Bio of our Speaker:

Mateusz Zawisza is a Senior Analytics Advisor to McKinsey & Company, where he's responsible for: 1) designing and implementing analytical use cases, 2) coaching & providing trainings on analytics and 3) supporting & advising on recruitment of analytical talent. He has served Polish & international clients from sectors of: retail, telecommunication, financial services & production, where he levered analytics to support day-to-day decisions made within functional areas of: Sales & Marketing, Operations and Risk. He's a PhD candidate & lecturer at Warsaw School of Economics as well as the co-author of analytics book titled: "Receptury w R". Mateusz is especially interested in the translation of analytical calculations into actual decisions.

14 May, 18:30 – 20:30

How chatbots and voice assistants can help in medical diagnosis?

Join our Wroclaw community of practitioners on Tuesdays in the Knowledge Center for interactive technical presentations by data scientists, tech entrepreneurs and experts. Adam Radziszewski will present how Infermedica handle NLP problems. Most difficult part is to detect and understand medical concepts in user input.

5 February 18:30 – 20:30

Best Friends 4 Ever: Data Scientists + Data Engineers

Data Scientist, Armin Reinert and Senior Data Engineer, Przemek Popowicz share their story of collaborating to create an optimal data ecosystem.

11 December, 18:30 – 20:30

How good is your model?

Alicja Gosiewska, researcher and data scientist and Agnieszka Ciepielewska, data engineer present Auditor: an R Package for Model-Agnostic Visual Validation and Diagnostics

20 November, 18:30 – 20:30

Lego Workshop: Agile Data Science

We’ve all heard of Agile for software development, but how is it used to improve collaboration, flexibility and adaptiveness on data science teams? McKinsey Senior Analyst Michal Olszanski and Data Scientist Krzysztof Lawecki will lead an interactive workshop that demonstrates how critical it is to think beyond the code.

23 October, 18:30 – 20:30

So Shiny! 

Data scientists Mikołaj Olszewski and Mikołaj Bogucki of iDash kick off our series with a presentation of the pros and cons of R Shiny and its optimal use in a production environment.

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